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Private and public clouds are good means for getting on-demand intensive computing resources. In such a context, selecting the most appropriate clouds and virtual machines (VMs) is a complex task. From the user’s point of view, the challenge consists in efficiently managing cloud resources while integrating prices and performance criteria. This paper focuses on the problem of selecting the appropriate clouds and VMs to run bags-of-tasks (BoT): big sets of identical and independent tasks. More precisely, we define new mathematical optimization models to deal with the time of use of each VMs and to jointly integrate the execution makespan and the cost into the objective function through a bi-objective problem. In order to provide trade-off solutions to the problem, we propose a lexicographic approach. In addition, we introduce, in two different ways, capacity constraints or bounds on the number of VMs available in the clouds. A global limit on the number of VMs or resource constraints at each time period can be defined. Computational experiments are performed on a synthetic dataset. Sensitivity analysis highlights the effect of the resource limits on the minimum makespan, the effect of the deadline in the total operation cost, the impact of considering instantaneous capacity constraints instead of a global limit and the trade-off between the cost and the execution makespan.

Time-and-Level-of-Use (TLOU) is a recently proposed pricing policy for energy, extending Time-of-Use with the addition of a capacity that users can book for a given time frame, reducing their expected energy cost if they respect this self-determined capacity limit. We introduce a variant of the TLOU defined in the literature, aligned with the supplier interest to prevent unplanned over-consumption. The optimal price-setting problem of TLOU is defined as a bilevel, bi-objective problem anticipating user choices in the supplier decision. An efficient resolution scheme is developed, based on the specific discrete structure of the lower-level user problem. Computational experiments using consumption distributions estimated from historical data illustrate the effectiveness of the proposed framework.

International audience; With the emerging deregulated electricity markets, a part of the electricity trading takes place in dayahead markets where producers and retailers place bids in order to maximize their profit. We present a price-maker model for strategic bidding from the perspective of a producer in Price Coupled Regions (PCR) considering a capacitated transmission network between local day-ahead markets. The aim for the bidder is to establish a production plan and set its bids taking into consideration the reaction of the market. We consider the problem as deterministic, that is, the bids of the competitors are known in advance. We are facing a bilevel optimization problem where the first level is a Unit Commitment problem, modeled as a Mixed Integer Linear Program (MILP), and the second level models a market equilibrium problem through a Linear Program. The problem is first reformulated as a single level problem. Properties of the optimal spot prices are studied to obtain an extended formulation that is linearized and tightened using new valid inequalities. Several properties of the spot prices allow to reduce significantly the number of binary variables. Two novel heuristics are proposed, the first applicable in PCR, the second for general formulations with Special Ordered Sets (SOS) of type 1. Our computational experiments highlights the risk of a loss for the bidder if some aspects usually not considered in the literature, such as Price Coupled Regions, or an accurate UC problem, are not taken into account. They also show that the reformulation techniques, combined with new valid inequalities, allow to solve much larger instances than the current state-of-the-art. Finally, our experiments also show that the proposed heuristics deliver very high quality solutions in a short computation time.

International audience; This study analyzes the scheduling of unpredictable fare inspections in proof-of-payment transit systems, where the transit operator chooses a collection of patrol paths (one for each patrol) every day with some probability in order to avoid any regularity that could be exploited by opportunistic passengers. We use a Stackelberg game approach to represent the hierarchical decision-making process between the transit operator and opportunistic passengers, whose decision on whether to evade the fare depends on the inspection probabilities set by the transit operator. Unlike previous work, we use an exact formulation of the inspection probabilities that allows us to develop new heuristics for the fare inspection scheduling problem, and to assess their solution quality in terms of their optimality gap.

The delegated portfolio management has been at the core of financial debates, leading to a growing research effort to provide modeling and solution approaches. This class of problems focuses on investors relying upon decentralized affiliates for the specialized selection of investment options. In this paper we propose a novel optimization framework for multi-market portfolio management, where a central headquarter delegates the market-wise portfolio selection to specialized affiliates. Being averse to risk, the headquarter endogenously sets the maximum expected loss (in the form of conditional value at risk) for the affiliates, who respond designing portfolios and retaining portions of the expected investment returns as management fees. In its essence, this problem constitutes a single-leader-multi-follower game, resulting from the decentralized investment design. Starting from a bilevel formulation, our results build on the equivalence with the high point relaxation to provide theoretical insights and numerical solution approaches. We show that the problem is NP-Hard and propose a decomposition procedure and strong valid inequalities, capable of boosting the efficiency of the computational solution, when instances become large. In the same line, optimality bounds exploiting overlooked properties of the conditional value at risk are deduced, to provide almost exact solutions with few seconds of computation. Building on this theoretical development, we conduct computational tests using comprehensive firm-level data from 1999 to 2014 on 7256 U.S. listed enterprises. These tests support the effectiveness of the decomposition procedure, as well as the one of the strong valid inequalities, improving the LP relaxation by up to 99.18 % .

Two-tier city logistics systems are playing a very important role nowadays in the management of urban freight activities. Although several city authorities have promoted different measures to foster the implementation of small urban consolidation centers in a two-tier system, only a few authors have addressed the joint problem of operating these facilities and providing services to customers. We show how the problem can be modeled as a new variant of the bin packing, for which we provide a mixed integer programming formulation and two heuristics that are shown to be quite effective in solving efficiently and to near optimality the problem. The application of our approach on real data from the city of Turin puts into highlight the superiority of the consolidation approach, including the bundle of goods from different providers, stockholding and other value-added logistics services, over the classical single-tier approach. In addition, the paper provides a thorough analysis of some emerging aspects of the on-demand economy, as the consideration of customers’ preferences and the integration of multiple delivery options. The managerial insights coming from this work will be part of the new Logistics and Mobility Plan to be activated in 2022 in the Piedmont region.

International audience; This paper presents a novel price setting optimization problem for an electricity supplier in the smart grid. In this framework the supplier provides electricity to a residential load aggregator using Time-and Level-of-Use prices (TLOU). TLOU is an energy pricing structure recently introduced in the literature, where the prices vary depending on the time and the level of consumption. This problem is formulated as a bilevel optimization problem, in which the supplier sets the prices that maximize the profit in a demand response context, anticipating the reaction of a residential load aggregator that minimizes total cost. These decisions are made in a competitive environment, while explicitly considering the aggregator's load shifting preferences and the level of consumption, and ensuring a user-friendly price structure. The optimization problem is reformulated as a single-level problem to be solved using off-the shelf solvers. We present computational experiments to validate the performance of TLOU, and provide insights on the relationship between the user's demand flexibility, the capacity profile and the resulting structure of prices. We show that the supplier's economical benefit is increased up to 10% through the implementation of this type of demand response program, while providing savings of up to 6% for the consumers.

International audience; Demand-side management (DSM) is a powerful tool to efficiently manage the consumption of energy. DSM relies on various techniques and means. In this work, we propose a trilevel energy market model for load shifting induced by time-of-use pricing. Four kinds of actors are involved: electricity suppliers (sell energy), local agents (buy, sell and consume), aggregators (buy and sell) and end users (consume). The interactions among these actors lead to a trilevel multi-leader–multi-follower game. Solving such games is known to be hard, thus we assume that the decision variables of all electricity suppliers but one are known and optimize the decisions of the remaining supplier. This leads to a single-leader–multi-follower game, which aims to compute the leader’s best response to the decisions of his competitors.The trilevel model is first formulated as a bilevel problem using an explicit formula for the lowest optimization level. Solution algorithms are developed in the optimistic case and in a variant named “semi-optimistic” approach leading to more robust solutions. Finally, numerical results highlight the efficiency of the methods and the sensitivity of the solutions with respect to the model parameters.

Near-optimality robustness extends multilevel optimization with a limited deviation of a lower level from its optimal solution, anticipated by higher levels. We analyze the complexity of near-optimal robust multilevel problems, where near-optimal robustness is modelled through additional adversarial decision-makers. Near-optimal robust versions of multilevel problems are shown to remain in the same complexity class as the problem without near-optimality robustness under general conditions.

In this paper, we address a day-ahead pricing and load balancing problem, within an environment involving self-scheduled users whose utilities are optimized via a smart grid. The model is formulated as a mixed integer bilevel program for which we propose a single level reformulation. Next, we design two heuristic algorithms which, by relying on the structure of the problem, provide high quality solutions in moderate computation time.

SCOPUS: ed.j, info:eu-repo/semantics/published

International audience; The improper integration of traditional transportation modes with low emissions vehicles can generate a price war that reduces the service quality, undermining the efficiency and the profitability of parcel delivery operators. This paper aims to provide managerial insights to design a win-win strategy for the co-existence of traditional and green business models. In doing so, we adopt a multi-disciplinary approach that integrates a qualitative analysis through a Lean Business methodology, named GUEST, with a quantitative analysis based on simulation-optimisation techniques. This kind of holistic vision has received little attention in the literature. The first analysis investigates the parcel delivery industry with an emphasis on the main business models involved, their costs and revenues structures, while the quantitative part aims to simulate the system and extract sustainable policies. In particular, results highlight that in deploying mixed-fleet policies, the decision-makers have to focus both on the environmental sustainability that benefits from the adoption of low-emission vehicles, and on the operational feasibility and economic sustainability of the two services. In this direction, the paper suggests some managerial insights concerning the split of the customer demand between traditional and green operators, according to the classes of parcels and geographical areas of the city.

International audience; To meet unbalanced demand, an energy provider has to include costly generation technologies, which in turn results in high residential electricity prices. Our work is devoted to the application of a bilevel optimisation, a challenging class of optimisation problems, in electricity market. We propose an original demand-side management model, adapt a solution approach based on complementary slackness conditions, and provide the computational results on illustrative and real data. The goal is to optimise hourly electricity prices, taking into account consumers' behaviour and minimizing energy generation costs. By choosing new pricing policy and shifting electricity consumption from peak to off-peaks hours, the consumers might decrease their electricity payments and eventually, decrease the energy generation costs.

E-commerce has been continuously growing in the last years to a primary retail market. Recently in France, the threshold of 1 billion of online transactions was overcome. Due to a high demand fluctuation of e-commerce, the workforce sizing for the logistic chain is a challenging problem. Companies have to develop good strategies to have a sustainable workforce size while guaranteeing a high-level service. In this paper, we consider the management of the workforce for a warehouse of an e-commerce company. Specifically, we address issues as i) How the workforce at the warehouse can be determined; ii) What is the daily operational production planning; iii) How the demand peaks can be smoothed, and the production maintained ideally constant over the time horizon. To provide answers to these issues, we introduce the Packaging and Shipping Problem (PSP). The PSP looks for a solution approach that jointly determines the workforce over a multi-period horizon and daily operational plans while minimizing the total logistics cost. We consider two strategies that aim to enhance the flexibility of the process and the efficiency of resources use: reassignment and postponement. To tackle the Packaging and Shipping Problem we propose a model, and a three-phase matheuristic. This heuristic is proved to be competitive with respect to the direct solution of the model with a commercial solver on real-life based instances.

International audience; Attended home delivery is a last-mile delivery service, where the customer must be present for the delivery. The classical delivery model utilizes couriers who serve customer requests through bicycles, motorcycles, cars and vans and that communicate via mobile phone. Most of the vehicles perform short-duration delivery routes with respect to the planning horizon due to the characteristics of the package, the delivery service, and the vehicle capacity. Each customer request consists of a pair of pickup and delivery locations (o-d pairs) associated with a tight delivery time window on which the delivery company and the customer agree. These requests are usually done with little or no notice and their frequency depend on several factors as the day of the week and the hour of the day. One major concern for last-mile delivery service companies is to deal with high variability in the demand as well with high concentration of the demands in some time slots. In this presentation, we present a revenue management approach in order to favor demand shifting from peak slots to off-peak slots and thus smooth out the parcel delivery demand curves. Indeed increasing capacity (by requiring the services of external couriers) is highly expensive. Nevertheless it is possible for the service provider to decrease his capacity cost by increasing some peak period prices and/or reducing off-peak period prices, and hence convince some customer to shift their demand to more available periods. Revenue management is the name given to "a series of techniques which are directed towards enhancing the firm's revenues". The objective of these techniques is not necessarily to stimulate product demand directly, but rather to better convert existing demand into higher revenues. The development of RM has been initially stimulated by the deregulation of the airline traffic in the United States in the late 1970s. As the impact of RM on the airline business has been dramatic, RM has now emerged as a best business practice in many industries. After the airlines, RM quickly spread to rental cars, hotels, and vacations… The last mile delivery service problem satisfies the assumptions to apply RM approaches. The products, i.e. the number of deliveries per time slot and per courier, are perishable and can be booked in advance. The capacity is limited and cannot be increased easily. The demand is composed of heterogeneous customers with a certain price sensitivity allowing customers segmentation. Finally the demand varies sharply with the time of the day and is mainly unknown a long time in advance. The proposed approach is divided into two phases each one corresponding to a different planning level: tactical and operational. The tactical planning aims to determine the available delivery capacity per time slot whereas the operational planning integrates RM techniques to smooth out the demand.

In this paper, we consider an energy provider whose goal is to simultaneously set revenue-maximizing prices and meet a peak load constraint. The problem is cast within a bilevel setting where the provider acts as a leader (upper level) that takes into account a smart grid (lower level) that minimizes the sum of users' disutilities. The latter bases its actions on the hourly prices set by the leader, as well as the preference schedules set by the users for each task. We consider both the monopolistic and competitive situations, and validate numerically the potential of this approach to achieve an ‘optimal’ trade-off between three conflicting objectives: revenue, user cost and peak demand.

The Bilevel Knapsack Problem (BKP) is a hierarchical optimization problem in which the feasible set is determined by the set of optimal solutions of parametric Knapsack Problem. In this paper, we propose two stages exact method for solving the BKP. In the first stage, a dynamic programming algorithm is used to compute the set of reactions of the follower. The second stage consists in solving an integer program reformulation of BKP. We show that the integer program reformulation is equivalent to the BKP. Numerical results show the efficiency of our method compared with those obtained by the algorithm of Moore and Bard

We propose an efficient dynamic programming algorithm for solving a bilevel program where the leader controls the capacity of a knapsack, and the follower solves the resulting knapsack problem. We propose new recursive rules and show how to solve the problem as a sequence of two standard knapsack problems.

Although leader-follower games have been around for quite some time in economics and game theory, their integration into the realm of optimization and operations research is recent. In this paper, we provide an overview of research in bilevel programming that was initiated at the University of Montreal and led to a large scale application in the field of revenue management.

International audience; In e-commerce business, the delivery of products is a crucial part for the success of an e-shop. An efficient delivery system should offer various services and predict customers behaviour. The latter are influenced by the price of a delivery service, but also by its quality (perceived through congestion effect induced by customers’ choices). In this study, we introduce a bi-level model to optimize a delivery system. At the upper level, the provider control services’ tariffs. At the lower level, users react by choosing their delivery service according to a disutility function which icorporates the provider tariff and the congestion effects. We model the customers’ reaction using stochastic user equilibrium (SUE). We also present a sensitivity analysis for the SUE that gives explicit expression of the derivatives of customers distribution with respect to services’ tariffs. Based on a local search that exploits the derivatives information, a new heuristic algorithm for a delivery services pricing problem is developed and compared to others existing methods.

International audience; Online retail have been continously growing in the last years. In France, during the secondquarter of 2013, online retail rose by 20% in comparison to same period of the year before.To reduce global cost, coordinating warehouse management and transport planning in a globaloptimization model was considered. In keeping with this trend, this work investigatesa new model for coordinating packaging and shipping operations. Orders are shipped using a set of delivery options, called channels. Joint Packaging and shippingenables us to define actions that influence the standard processing in term of managing peakof activity : packaging postponement, channel change. Uncertainty of orders arrival at warehouse guided us to consider stochastic optimization techniques in a rolling horizon schema. Numerical experimentation are conducted using realistic data provided by a major logistic services company.

International audience; In this paper, we consider the revenue maximization problem of a rail freight transportation company or an intermodal marketing company selling freight transportation services. We propose a revenue management (RM) policy to dynamically accept transportation requests or reject them in favor of some future forecasted transportation demands with higher potential profit. In the proposed load acceptance system, we explicitly take the network structure into account. We analyze solutions obtained from numerical simulations and conclude on the promising results shown by the RM system.

This article traces the evolution of ambulance location and relocation models proposed over the past 30years. The models are classified in two main categories. Deterministic models are used at the planning stage and ignore stochastic considerations regarding the availability of ambulances. Probabilistic models reflect the fact that ambulances operate as servers in a queueing system and cannot always answer a call. In addition, dynamic models have been developed to repeatedly relocate ambulances throughout the day. 2002 Elsevier Science B.V. All rights reserved.

We propose fast heuristics for large-scale covering-location problems in which the set of demand points is discrete and the set of potential location sites is continuous. These heuristics are compared on a set of 152 real-life instances arising in cytological screening.

International audience; The goal of this paper is to introduce bounded rational behaviors in a competitive queuing system. Furthermore, we propose a realistic queuing model for two last-mile delivery services in which consumers are in competition. This work is derived from a real-world e-commerce application. We study the problem using a game theoretical point of view: the e-consumers are interacting through the last-mile delivery service system creating congestion for each other. Specifically, we focus our analysis on several equilibrium concepts from congestion/routing games: Wardrop and Logit equilibria. The difference in these equilibrium concepts is on the rationality level of players in the game. We are able to prove the existence and uniqueness of both equilibria. We compare them through a new metric called the Price of Rationality and we also compare each one to the social optimum solution through the Price of Anarchy. Some numerical results are presented in order to illustrate the theoretical results obtained.

We consider a bilevel programming formulation of a freight tariff-setting problem where the leader consists in one among a group of competing carriers and the follower is a shipper. At the upper level, the leader's revenue corresponds to the total tariffs levied, whereas the shipper minimizes its transportation cost, given the tariff schedule set by the leader. We propose for this problem a class of heuristic procedures whose relative efficiencies, on small problem instances, could be validated with respect to optimal solutions obtained from a mixed integer reformulation of the mathematical model. We also present numerical results on large instances that could not be solved to optimality by an exact method.

International audience; This work focuses on an improved exact algorithm for addressing an NP-hard network pricing problem. The method involves an efficient and partial generation of candidate solutions, a recursive scheme for generating improved upper bounds, and a column generation procedure for solving the network-structured subproblems. Its efficiency is assessed against both randomly generated instances involving three distinct topologies as well as instances based on real life situations in telecommunication and freight transportation.

International audience

In this paper, we present a model for dynamic traffic assignment in urban transportation networks. The transportation demand is given by the number of users traveling from the same origin to the same destination and having the same desired arrival time. The model determines for each user both the path and the departure time so that he cannot decrease his disutility by unilaterally changing his departure time or/and his path. The disutility measure is given by a generalized travel time function taking into account total path travel time and schedule delays. Departure time choices are restricted to a finite set of instants, but users with same choice of departure time and same first arc on selected path are loaded uniformly on the network during a time interval whose lower bound is the chosen departure time. A finite dimensional variational inequality formulation of the problem is given and the existence of a dynamic user equilibrium is proved. A heuristic method is proposed to compute a dynamic user equilibrium. Numerical results are provided.

We consider the problem of determining a set of optimal tolls on the arcs of a multicommodity transportation network. The problem is formulated as a bilevel mathematical program where the upper level consists in a firm that raises revenues from tolls set on arcs of the network, while the lower level is represented by a group of users travelling on shortest paths with respect to a generalized travel cost.

International audience

International audience

In this thesis, we aim to develop models and decision making algorithms to design simultaneously several sustainable logistics and transportation chains between origins and destinations given a logistics and transportation network. First, we investigate the state of the art of decentralized logistics and transportation systems, and survey the Winner Determination Problem. Then, we study the Bundle Construction Problem to construct transportation routes, which is solved by an exact bi-directional dynamic programming. Next, we merge the Bid Generation Problem and Winner Determination Problem through the bi-level concept, the centralized and decentralized bi-level problems are then solved by Linearization procedure and Best Response procedure, respectively.; Dans cette thèse, nous avons pour objectif de développer des modèles et des algorithmes de prise de décision pour concevoir simultanément plusieurs chaînes de logistique et de transport durables entre origines et destinations compte tenu d'un réseau de logistique et de transport. Tout d'abord, nous étudions l'état de l'art des systèmes de logistique et de transport décentralisés et étudions le problème de la détermination des gagnants. Ensuite, nous étudions le problème de construction de paquets pour construire des itinéraires de transport, qui est résolu par une programmation dynamique bidirectionnelle exacte. Ensuite, nous fusionnons le problème de génération d'enchères et le problème de détermination du gagnant via un concept à deux niveaux, les problèmes à deux niveaux centralisés et décentralisés sont ensuite résolus par la procédure de linéarisation et la procédure de meilleure réponse, respectivement.

The objective of this thesis is to develop and study mathematical models of economical exchanges between energy suppliers and consumers, using demand-side management. On one hand, the suppliers offer time-of-use electricity prices. On the other hand, energy consumers decide on their energy demand schedule, minimizing their electricity bill and the inconvenience due to schedule changes. This problem structure gives rise to bilevel optimization problems.Three kinds of models are studied. First, single-leader single-follower problems modeling the interaction between an energy supplier and a smart grid operator. In this first approach, the level of details is very high on the follower’s side, and notably includes a stochastic treatment of distributed generation. Second, a multi-leader multi-follower problem is studied from the point of view of the best response of one of the suppliers. Aggregators are included in the lower level. Two new resolution methods based on a selection of Nash equilibriums at the lower level are proposed. In the third and final part, the focus is on the evaluation of noncooperative equilibriums for this multi-leader multi-follower problem.All the problems have been studied both from a theoretical and numerical point of view.; L’objectif de cette thèse est de développer et étudier des modèles mathématiques d’échanges économiques, basés sur la flexibilité de la demande, entre fournisseurs et consommateurs d’électricité. D’une part, des fournisseurs d’électricité offrent des prix dépendant de l’heure de consommation. D’autre part, des consommateurs adaptent leur usage, minimisant leur facture et le désagrément lié aux changements de consommation induits. La structure de ces problèmes correspond à des problèmes d’optimisation bi-niveau. Trois types de modèles sont étudiés. Tout d’abord, l’interaction entre un fournisseur et un opérateur de smart grid est modélisée par un problème à un seul meneur et un seul suiveur. Pour cette première approche, le niveau de détails du suiveur est particulièrement élevé, et inclut notamment une gestion stochastique de la production distribuée. La meilleure réponse d’un fournisseur dans un modèle à plusieurs meneurs et plusieurs suiveurs fait l’objet de la seconde partie de la thèse. Celle-ci intègre aussi la possibilité d’avoir des agrégateurs comme suiveurs. Deux nouvelles méthodes de résolution reposant sur la sélection d’équilibres de Nash entre suiveurs sont proposées. Enfin, dans une troisième et dernière partie, on se focalise sur la recherche d’équilibres non coopératifs pour ce modèle à plusieurs meneurs et plusieurs suiveurs.Tous les problèmes abordés dans cette thèse le sont non seulement d’un point de vue théorique, mais également d’un point de vue numérique

This thesis is concerned with the problem of pricing for a long-haul full load goods transportation. More precisely, we are interested in the situation where each vehicle delivers single request at a time. In this environment, we study the problem of pricing and valorization of unutilized capacity between two carriers. The first carrier B, cannot serve all the transportation requests and he thus needs to use outsourcing : second carrier A or his competitors. Carrier A, has to define the prices for carrier B transportation requests. Once carrier A has given its prices for the operations, it is B's decision to turn to A or to another carrier. This sequential and non-cooperative decision-making process can be adequately represented as a bilevel program : carrier B (the follower) wants to minimize transportation cost while A (the leader) seeks to maximize the revenue. Carrier A explicitly incorporates the reaction of carrier B in his optimization process. Two types of models have been proposed : the bilevel mono-objective model and the bilevel biobjective model. More precisely, two objectives are simultaneously considered for the leader problem : the maximization of revenue and balancing the free load length (limiting the free load distances). We propose exact methods to solve moderate size instance of the problem and the heuristics to solve large-scale instances in reasonable time.; Nous étudions dans cette thèse un problème de tarification pour le transport longue distance de marchandises à charge pleine. Plus précisément chaque véhicule livre une seule demande à la fois. Dans cet environnement, nous étudions une problématique de tarification et de valorisation de la capacité inutilisée entre deux transporteurs. Le premier, le transporteur B, possède une clientèle qu'il ne peut satisfaire en totalité, et devra se résoudre à sous-traiter une partie à la concurrence. Le deuxième transporteur, le transporteur A, concurrent mais partenaire privilégié du premier, propose des tarifs pour combler les demandes insatisfaites du transporteur B. Nous sommes en présence d'un processus de décision séquentiel, non coopératif, hiérarchisé que nous modélisons par une approche de programmation bi-niveau : au premier niveau le transport A (le meneur) a pour objectif de maximiser son revenu en fixant des tarifs, et en prenant explicitement en compte la réaction du transporteur B (le suiveur) qui a pour objectif de satisfaire toutes ses demandes à coût minimal. Deux types de modèles ont été proposé : un modèle bi-niveau mono-objectif et un modèle bi-niveau bi-objectif. Plus précisément deux objectifs sont pris simultanément en compte pour le problème de haut niveau : la maximisation du revenu et l'équilibrage de la distance parcourue à vide. Des méthodes exactes ont été proposées, pour résoudre les instances de petites tailles et des heuristiques sont définies pour obtenir de bonnes solutions en des temps raisonnables sur des instances de plus grande taille.

This thesis is concerned with the problem of pricing for a long-haul full load goods transportation. More precisely, we are interested in the situation where each vehicle delivers single request at a time. In this environment, we study the problem of pricing and valorization of unutilized capacity between two carriers. The first carrier B, cannot serve all the transportation requests and he thus needs to use outsourcing : second carrier A or his competitors. Carrier A, has to define the prices for carrier B transportation requests. Once carrier A has given its prices for the operations, it is B's decision to turn to A or to another carrier. This sequential and non-cooperative decision-making process can be adequately represented as a bilevel program : carrier B (the follower) wants to minimize transportation cost while A (the leader) seeks to maximize the revenue. Carrier A explicitly incorporates the reaction of carrier B in his optimization process. Two types of models have been proposed : the bilevel mono-objective model and the bilevel biobjective model. More precisely, two objectives are simultaneously considered for the leader problem : the maximization of revenue and balancing the free load length (limiting the free load distances). We propose exact methods to solve moderate size instance of the problem and the heuristics to solve large-scale instances in reasonable time.; Nous étudions dans cette thèse un problème de tarification pour le transport longue distance de marchandises à charge pleine. Plus précisément chaque véhicule livre une seule demande à la fois. Dans cet environnement, nous étudions une problématique de tarification et de valorisation de la capacité inutilisée entre deux transporteurs. Le premier, le transporteur B, possède une clientèle qu'il ne peut satisfaire en totalité, et devra se résoudre à sous-traiter une partie à la concurrence. Le deuxième transporteur, le transporteur A, concurrent mais partenaire privilégié du premier, propose des tarifs pour combler les demandes insatisfaites du transporteur B. Nous sommes en présence d'un processus de décision séquentiel, non coopératif, hiérarchisé que nous modélisons par une approche de programmation bi-niveau : au premier niveau le transport A (le meneur) a pour objectif de maximiser son revenu en fixant des tarifs, et en prenant explicitement en compte la réaction du transporteur B (le suiveur) qui a pour objectif de satisfaire toutes ses demandes à coût minimal. Deux types de modèles ont été proposé : un modèle bi-niveau mono-objectif et un modèle bi-niveau bi-objectif. Plus précisément deux objectifs sont pris simultanément en compte pour le problème de haut niveau : la maximisation du revenu et l'équilibrage de la distance parcourue à vide. Des méthodes exactes ont été proposées, pour résoudre les instances de petites tailles et des heuristiques sont définies pour obtenir de bonnes solutions en des temps raisonnables sur des instances de plus grande taille.